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Found 4 papers, 2 papers with code
Latent Temporal Flows for Multivariate Analysis of Wearables Data
Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows.
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis
We introduce Latent Temporal Flows (\emph{LatTe-Flows}), a method for probabilistic multivariate time-series analysis tailored for high dimensional systems whose temporal dynamics are driven by variations in a lower-dimensional discriminative subspace.
Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously.
Methodology Neurons and Cognition Applications
Exact Spike Train Inference Via $\ell_0$ Optimization
For each neuron, a fluorescence trace is measured; this can be seen as a first-order approximation of the neuron's activity over time.
Applications Neurons and Cognition
